DA0-002 Data Concepts and Environments Practice Question
A data engineer is designing a data warehouse for a retail company. The fact table must record each sale transaction, including product ID, store ID, date, and quantity sold. The product details (name, category, price) are stored in a separate table. This design is an example of which data modeling concept?
⚠ Common exam trap
A common mix-up: candidates confuse star schema with snowflake schema, but the key differentiator is whether dimension tables are further normalized (snowflake) or kept denormalized (star), and this question's single product table clearly indicates a star schema.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Star schema
This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Star schema
Why this is correct
Correct: fact table linked to dimension tables.
- ✗
Data lake
Why it's wrong here
Data lake stores raw data, not structured schema.
- ✗
Normalization
Why it's wrong here
Normalization is a broader process, not a specific schema.
- ✗
Snowflake schema
Why it's wrong here
Snowflake would further normalize product dimension into sub-dimensions.
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